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Hyperspectral image classification method based on spectrums and neighbourhood information dictionary learning

A hyperspectral image and dictionary learning technology, applied in the field of hyperspectral image classification and image processing, can solve the problems of poor acquisition of neighborhood information and poor classification effect of homogeneous regions, and achieve fine classification and classification effect Good, accurate classification effect

Active Publication Date: 2014-06-25
XIDIAN UNIV
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Problems solved by technology

Although this method can quickly classify hyperspectral images, it still has the disadvantage that the neighborhood information of the samples cannot be obtained well by comparing the Euclidean distance to obtain the neighborhood sample set matrix, resulting in poor classification results in homogeneous regions. not good

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  • Hyperspectral image classification method based on spectrums and neighbourhood information dictionary learning

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Embodiment Construction

[0033] The present invention will be further described below in conjunction with the accompanying drawings.

[0034] Refer to attached figure 1 , the implementation steps of the present invention are as follows:

[0035] Step 1, input hyperspectral image.

[0036] Input the hyperspectral image to be classified, and set each pixel in the input hyperspectral image as a sample.

[0037] Step 2, get the sample set.

[0038] First, the coordinate transformation method is used to process the hyperspectral image, and the samples of each dimension of the hyperspectral image are arranged into a row vector to form a two-dimensional matrix, and the sample set of the spectral domain of the hyperspectral image is obtained.

[0039] Secondly, set a 7×7 sample window in the spectral domain of the hyperspectral image, and perform mean filtering on the sample set in the spectral domain of the hyperspectral image to obtain a sample set in the hyperspectral image neighborhood.

[0040] Step ...

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Abstract

The invention discloses a hyperspectral image classification method based on spectrums and neighbourhood information dictionary learning. The defects that in the prior art, only the spectral information of a hyperspectral image is utilized, and hyperspectral image neighbourhood information cannot be effectively utilized for classification are overcome. The method comprises the implementation steps that (1) hyperspectral images are input; (2) a sample set is obtained; (3) a training sample set and a testing sample set are determined; (4) dictionary learning is carried out; (5) the sparse coefficient of the test sample set is obtained; (6) the sparse coefficient is weighted; (7) the hyperspectral images are classified; (8) the classified images are output. The method has the advantage that the classification effect is more precise on the edges and homogeneous regions of the hyperspectral images, and can be used for classifying the hyperspectral images.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a hyperspectral image classification method based on spectral and neighborhood information dictionary learning in the technical field of hyperspectral image classification. The invention can be used to classify the hyperspectral images. Background technique [0002] The improvement of spatial domain and spectral domain resolution of hyperspectral images provides more abundant information for classification, but also brings great challenges. Traditional classification methods, including maximum likelihood classification, decision tree classification, artificial neural network classification, and support vector machine classification, all only classify features from the spectral domain level. However, hyperspectral remote sensing data not only contains rich spectral information of surface objects, but also has specific description and expression of surface object ch...

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Application Information

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IPC IPC(8): G06K9/66
Inventor 白静焦李成勾珍珍李甜甜王爽张向荣马文萍马晶晶
Owner XIDIAN UNIV
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